Sinusoidal noise exists in many systems. For instance, the input signals for medical devices, such as an electrocardiograph (ECG), are often interfered with by electrical power supply line networks. For another instance, the read/write head in a disk drive deviates from the desired tracking trajectory due to disk eccentricity. This interference, or deviation, is generally caused by sinusoidal noise.
In each of these types of systems, it is desirable to eliminate such spurious signals, and isolate the desired signal, so that the output of a circuit which processes the signal is a true representation of the input signal without noise. In general, there are two methods to remove sinusoidal noise in a system. One method is to insert a notch filter at the noise frequency in series into the signal flow path. Another method is to detect the sinusoidal noise, and then to subtract it from the contaminated signal.
Both the serial notch filter and the signal subtraction methods of removing sinusoidal noise have drawbacks. For example, one problem with using a serial notch filter is that with the elimination of noise at the notch frequency, the frequency component of the desired signal at the notch frequency is eliminated as well. This is particularly unacceptable in ECG, where any clinical information of the patient, including signals at the filtered frequency, should be examined as the base for diagnostic and treatment. In addition, using serial notch filters in ECG applications can cause ringing in the ECG waveform, which can result in an incorrect interpretation and/or analysis of the ECG signal.
In the noise subtraction method, there are generally three approaches in implementation: Adaptive Noise Cancelling (ANC), Adaptive Feedforward Cancellation (AFC) and Internal Mode. Adaptive Noise Cancelling, in which the noise is considered uncorrelated with the input signal but correlated with a known reference signal, generally averages the signal over some amount of time to cancel the noise. This ANC approach relies upon an additional reference signal that may or may not be known, and also relies on an averaging approach in the concept of least-square. However, averaging signal over time is considered to risk change of some signal characteristic, e. g. removal or distortion of nonrepetitive signals, which may bear clinically relevant physiological dynamic information of the original ECG signal.
In Adaptive Feedforward Cancellation, noise is canceled by a signal expressed as a linear combination of sine and cosine regressors and two unknown parameters, in which the amplitude and phase of the sinusoidal noise are embedded. With this linear feature, an adaptive rule is designed to update the unknown parameters, thereby causing the output of the signal to converge to the noise in amplitude and phase. The regressors that have the noise frequency information embedded are usually implemented by look-up tables. Using this AFC approach, however, different look-up tables are needed for noises with different frequencies. For example, to estimate the higher harmonic noises, two additional look-up tables are needed for every harmonic, thereby rendering such implementations complex and expensive.
The Internal Mode approach uses trigonometric features to generate a sinusoidal signal that holds the information of amplitude and phase in the mode itself. The frequency information expressed in a parameter in the internal mode is generally required to be known and preset. Because the frequency is preset, it is claimed that this internal model is equivalent to a standard notch filter and does not provide for parameter adaptation. From the functional point of view, all above described approaches can be seen as notch filters in the sense that they attempt to remove the noise signal at the notch frequency.
Apart from the various problems with the methods described above, a common precondition to employing any of the above-described methods is that the frequency of the noise signal to be detected and removed is both constant and known. However, this requirement of prior knowledge for the noise frequency cannot always be met. In some cases, the noise frequency may change, and may be unknown to the user. For example, in the case of power line interference observed on ECG signals, for instance, there are different power line frequencies in different regions. For example, 60 Hz is used in North America, whereas 50 Hz in Europe and China. Because ECG users cannot be assumed to know the power line frequencies present in a particular region, and because the same ECG machine might be used or sold in different regions, ECG manufacturers are generally required to create systems that are capable of being used in any region.
One particular example of the output of an ECG machine is illustrated in FIG. 1. That figure illustrates an ECG report 10 for an ECG signal taken using a portable ECG CP50 machine manufactured by Welch Allyn, Inc. of Skaneateles Falls, N.Y. That device uses the internal mode approach, similar to that discussed above, in which a sinusoidal noise at a preset frequency (60 Hz in this example) signal can be filtered. In this example, the ECG is used in a country having a 50 Hz power supply. As illustrated, the ECG report shows power line noises (illustrated best in the magnified portion 12 of the report 10) that are not eliminated because of the difference between the preset frequency to the internal mode and the local power line frequency. As discussed above, the internal mode, as well as the various other approaches for removing periodic noise, are not well adapted to this scenario, in which differing power signal frequencies may be encountered.
In addition to the problem of power signals having different intended frequencies, it is also possible for some variance in a power line frequency to occur. For example, Standard EN50160 specifies a maximum power network frequency variations in countries forming the European Union (EU) as ±1% for 95% of a week, and +4%, −6% for a full week. This means that networks in EU might have a frequency variation of about 4% high, or 6% low, for periods of up to 5% of a week, that is, 8.5 hours. Moreover, there are some parts of the world where the electrical power supply is even worse, resulting in larger frequency variations than those specified in existing regional standards.
For these and other reasons, improvements in existing ECG machines and noise filters are desired.